\chapter{Plan for Completion}
This chapter presents the plan of action for the remainder of the PhD programme. It outlines the projects and their proposed outcomes, strategies for recruitment of participants, expected contributions, thesis chapters, future skills development and potential avenues for submission to conferences and journals.

Table \ref{table:rp} shows the breakdown of the proposed plan for completion in a Table. 

\section{Studies} 
The PhD thesis shall comprise of four studies. Over the next two years three studies will be undertaken, each might build upon the findings of the previous one or conducted as a whole new project. The studies will be structured as follows:

\subsection{Study One: Social Music Recommender Systems}
This experiment is explained as pilot study in Chapter \ref{chapter4}. It was conducted to gather data set but did not quite work for that purpose. However, there were interesting findings which have been presented in Chapter \ref{chapter4}. The lessons learnt and findings of the experiment will be submitted to CMMR 2012.
%too much choice harms well being causing regret, anxiety and failed expection, self blame
\subsection{Study Two: Multimodal Music Mood Classification for Music Recommender Systems}
Music Recommender System are developed to generate relevant recommendations based upon users' music collections or listening habits. The tools will build on research into extraction of metadata to allow music to be identified that is similar in sound and mood to a particular piece. The feature extraction of tracks accessible from ILM shall be done using Sonic Annotator. The audio similarity tools developed at Centre for Digital Music shall be used to measure similarity between songs, based on timbral sound features. Then using the Last.fm/We7 API, the mood-related terms shall be searched and compared with tag-track associated to commercial music by our system, in order to compare the two system with similar songs. Also, it is interesting to see whether experts and non-experts annotated metadata yield different results in terms of music recommendations.

%SAWA is a standard plugin API for audio analysis and a Semantic Web ontology based representation for returning the results of the analysis. SAWA demonstrates audio feature extraction technology developed at the Center for Digital Music. SAWA can be used as a batch feature extractor, for example, for automatically generating a small reference data set in RDF format. The resulting files can be loaded in a suitable RDF database and queried over, they may be browsed using a Semantic Web browser (e.g. the OpenLink RDF browser) or loaded and visualised with Sonic Visualiser a tool for studying the content of audio files. Mood identification based on audio similarity to production music with mood metadata. The results of this research will be built into new visualization and recommendation tools, incorporated into the ILM Desktop Music Library system, to give recommendations to producers from both commercial music and production music. Prototypes: A first prototype (P1) using existing c4dm music similarity and recommendation technology will be deployed quickly, and subject to closely monitored user-testing by BBC content producers. A second prototype (P2) will incorporate the results of the mood similarity research and user testing to produce the final demonstrator prototype. Resources: The project will use three sources of music: (a) the ILM commercial library (digitized part: over 1 million digitised songs, tagged with “standard” metadata); (b) the ILM production music library 
%(tagged with musicological metadata e.g. instrumentation and psychological metadata, e.g. mood); (c) BBC archive (digitized part: 1 million hours of material, both audio-only and audio-visual). Workplan:  (See Appendix B for more details and Gantt chart) * WP1:  Ingestion of material from BBC and ILM collections (months: m1-m3, continues). Initial import  and processing of music from ILM libraries and BBC archive. * WP2:  First prototype P1: build (m1-m5). Construction of prototype based on existing c4dm SoundBite technology. * WP3: P1: test  & evaluate (m6-m8, cont to m12). Deployment and evaluation by BBC content producers and other ILM customers for early feedback. * WP4: Research & develop 'enhanced similarity' algorithms (m1-m15). Develop audio-based mood similarity engine (BBC) and hybrid audio-similarity/metadata-tag mood similarity engine (c4dm). * WP5: Research & develop visualization algorithms (m1-m15). Develop methods for visualising similarity within programmes and between musical tracks. * WP6: Main prototype P2: build (m10-m13). Incorporate tools from WP4 and WP5 into second prototype tool. *WP7: P2: test & evaluate  (m14-m16, cont to m18). Deployment and evaluation by BBC content producers and other ILM customers. * WP8: P2 revisions  (m15-m17). Adjustment to algorithm parameters, visualisations and final interfaces based on user feedback. * WP9: Management  (m1-m18) Management: The project will be managed by the BBC using the PRINCE2 methodology. The project manager will co-ordinate the reporting to the TSB, oversee the development of the work-packages, monitor the risk register, update the exploitation plan when needed and organise project meetings.  The project manager will be in regular contact with the project lead in each organisation. The project leads will oversee their organisation’s progress, managing their part in the deliverables and reporting to the project manager.  There will be monthly meetings between project leads (some via videolink) to go over technical details and monitor progress.  Quarterly project meetings will be face to face, with project updates presented to the PMO and project issues discussed.  The exchange of emails relating to technical issues as they arise will enable clear communication of progress on a day to day basis, with a mailing list set up to ensure everyone is kept up to date. Due to the relatively close proximity of the three organisations, informal visits can happen regularly with little overhead.

%Q6) Innovation:  This project goes beyond state-of-the-art capabilities for  automatically extracting music similarity metadata, such as simple 4-mood detectors (e.g. US Patent 7396990). Our approach also avoids the type of “cold start” problem found in systems based on user playlists, such as Apple iTunes Genius and last.fm, which would be a particular difficulty with the use of unfamiliar production music. c4dm is aware of the latest research in this area through e.g. conferences such as ISMIR, SMC and AES. BBC will develop new audio-based mood similarity methods, using audio features captured as metadata, leveraging mood information currently being captured from theme tunes. c4dm will develop a new hybrid audio-similarity/metadata-tag based mood similarity engine based on 'moodby-proxy'. This will compute mood similarity of commercial music in a proxy domain, ILM’s production music collection, which has been reliably labelled with mood and other semantic metadata by the ILM team. We expect to patent this technology. Q7) Risk (See Appendix B for full table) * Novelty of approach to similarity-based recommendation (Technical, L/M):  Expertise and track record of c4dm in field, with experience from existing SoundBite recommendation technology. * Variable similarity matching across musical genres (Technical, M/H): Investigate two complementary  approaches: audio-based mood similarity (BBC) and hybrid audio similarity/metadata-tag based mood similarity (c4dm). Adapt algorithms and tune parameters for different genres. Increase user interaction for e.g. genre identification. * Final tool unsuited to target users (Technical, M/M): Develop and test early prototype (P1, WP2,3) to get early user feedback. Ensure wide range of stakeholders in diverse areas of BBC, content producers and ILM customers aware of the project for initial advice. * No commercial requirement for tools (Commercial, L/M): Knowledge of demand from BBC content producers and ILM customers. Early discussions with independent producers. * Different working methodologies among partners (Managerial, L/M): Managerial structure (see Appendix B) and workpackage ownership mitigates this risk. Clear reporting mechanisms and accountability. * Increased energy use on computing resources (Environmental,  L/L): Retain computed metadata to avoid unnecessary re-computation.
%Q8 Consortium (See Appendix C) The consortium consists of three partners: the BBC , ILM and QMUL. - BBC: The BBC's Research & Development department has a long history (over 60 years) of innovative and leading edge R&D work in the field of broadcasting. They are currently increasing their efforts in the audio field, and aim to combine with their work in programme metadata. The engineers that will work on this project have expertise in audio signal processing and metadata generation. The rest of the BBC can benefit from this work as rich, reliable metadata is becoming more important in programme making and broadcasting.
%ILM: I Like Music is an award winning Music Service Provider operating successfully in both B2B and B2C markets, and has expertise in music licensing and software platform development. ILM has been selected as the sole provider of online music to the BBC for the next five years, in competition with iTunes and other providers of music, demonstrating its depth of catalogue and endorsement of its technical delivery platform.

%The importance of contextual information has been recognized by researchers and practitioners in many disciplines, including e-commerce personalization, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management. While a substantial amount of research has already been performed in the area of recommender systems, the vast majority of existing approaches focuses on recommending the most relevant items to users and does not take into account any additional contextual information, such as time, location, weather, or the company of other people. More research in this area is required for coping with the dynamic world of web. Currently, music recommender systems make use of the user provided information in order to generate relevant recommendations. Our attempt in the current doctoral research is to find ways to model the users in order to make better recommendations addressing personal needs and striving to meet the user’s expectations as much as possible.\\

%The thesis is based on many previous contributions, illustrated by the scientific articles (10 published at Springer, IEEE Computer Society Press, Inderscience Publishing House or IGI Global, other 15 had appeared in the international conferences proceedings, and 14 had appeared in national publications), and by the participation to two international projects.
%The strengths of social media are appreciated widely. The envisioned semantic web is gradually moving closer to becoming a reality. But that for obvious reasons is subject to satisfactory progress over certain period of time because apparently, if people do not see sufficiently good results they began losing interest in the subject matter. Therefore, the current World Wide Web needs to make a shift towards the Semantic Web in somewhat quicker manner. Though, there are significant semantic repositories and applications developed over the last decade such as LinkedData\footnote{\url{http://www.linkeddata.org/}}, Dbpedia\footnote{\url{http://www.dbpedia.org/}}, Jamendo\footnote{\url{http://www.jamendo.org/}}, Mufin player\footnote{\url{http://www.mufin.org/}}, Musicovery\footnote{\url{http://www.musicovery.com/}}, etc. Web Music Communities like Last.fm\footnote{\url{http://www.last.fm/}}, Pandora\footnote{\url{http://www.pandora.com/}}, and Ping\footnote{\url{http://www.apple.com/itunes/ping}} are playing vital role in helping music listeners to build relations with similar music-listeners, get recommendations based on their current music collections and much more. 
%I believe that two important aspects of serendipity in human life are social encounters and language. These two aspects are intertwined as we learn most of our language by interacting with other people. My goal in this thesis is to show that using social network data in building navigation and recommendation tools fosters new discoveries of resources and terminology. \section{Concepts}I introduce two concepts to experiment with the use of social network data for music discovery, namely tag navigation The aim of the research is to enhance the performance of the current MRS by modeling the intentions of the user in order to meet user’s expectations effectively. 

\subsection{Study Three: Development of Mood Ontology to bridge Semantic Gaps in Music Recommender Systems}
Music mood describes the inherent emotional expression of a music clip. It is helpful in music understanding, music retrieval, and some other music-related applications. In this study, we intend to develop a mood ontology to reduce mood classification problem to reasoning and querying over the ontology. The purpose is to deal with local/closed world to provide efficient querying in ontologies in relation with the Music Ontology to utilise the network effect between the vocabularies of mood and music. Ontologies are effectively used to link terms and partial mapping. One good application is to illustrate the effect the semantic web can have on music recommendations, once data has become semantically structured. We are going to look at semantic wrappings as a semantically rich information source that when related to music files has many advantages and creates a whole new web of potential applications. The Mood Ontology and Music Ontology will be interlinked to achieve transparency in music recommendations. Explanation of recommendations can increase the level of confidence of users in the system. As already discussed, recommendation is a complex problem due to context blindness. Applications are gaining a new dimension by integrating the semantic web technologies. Our approach is enrolled in this direction of enhancing the traditional personalization techniques with semantic web technologies in order to eliminate the user intervention requested by the social web applications.
% RDF graph cannot be said to have unique interpretation as indicated by David K. Lewis in RDF Sematics Specification.

\subsection{Study Four: Evaluation of Mood Music Recommender Systems}
The evaluation of mood music recommender systems shall be undertaken to measure the effectiveness of our approaches. Study two shall be evaluated by the music producers. The results of this research will be built into new visualization and recommendation tools, incorporated into the ILM Desktop Music Library system, to give recommendations to producers from both commercial music and production music. BBC content producers and other ILM customers aware of the project shall be very helpful for initial advice. They can give feedback and their interaction with this system shall help evaluate the success of the recommender system.
For study three, we shall evaluate the mood ontology in terms of its coverage and interlinking with music ontology.
%The barriers between traditional technical musical functions (composition software, instruments, audio production tools, album-based physical delivery of recordings, listening devices) tend to vanish in the digital world continuum; this facilitates the emergence of new practices in which perception (listening) and action (personal organization, selection, re-production, production, performance, sharing, and distribution) are interrelated and feed each other, as in many spheres of human activity. The proposed studies are intended to demonstrate the technical feasibility of such advanced manipulation interfaces. It paves the way for future music delivery and access modes, when electronic distribution will have replaced the current model based on physical supports of audio recordings, freeing the digital coding of musical contents from the constraints of these supports and enabling extended digital musical representations and richer user experiences.

\section{Recruitment of Participants}
I aim to recruit the participants for my studies via web application of ILM for study two.

\section{Expected Contributions}
The outcome of this research will improve the recommender systems specifically targeting the Music domain. The research will be disseminated at various platforms. Study 2 shall form the foundation for the framework of a better Music Recommender System. The third study utilises semantic web for t. 
\section{Thesis Outline} 
\label{sec:ThesisOutline}
The thesis will expand on this document, and be written continuously throughout the next two years, as the relevant sections reach conclusion. The final six months of the PhD will be allocated to the completion of the written thesis. Proposed chapters for the Doctoral Thesis are as follows:  
\begin{itemize}
\item Introduction 
	\begin{itemize}
		\item Research Questions
		\item Structure of the Thesis
		\item Contributions
		\item Publications
	\end{itemize}
\item Background and Literature Review
	\begin{itemize}
		\item Social Music Recommender Systems
				\begin{itemize}
					\item Music and Social Web
							\begin{itemize}
								\item Methodology
								\item Experiment
								\item Results
							\end{itemize}
					
					\item Software Application Discussion
							\begin{itemize}
								\item SDLC
								\item Evaluation Metrics
							\end{itemize}

				%	\item Ecological Validity
					\item Summary of key points in Social Music Recommendations Research	
				\end{itemize}
	\end{itemize}
\item Semantic-Aware and Transparent Music Recommender Systems
	\begin{itemize}
		\item Methodology
		\item Experiment
		\item Results
	\end{itemize}
\item Mood Ontology and its interlinking with Music Ontology to Bridge Semantic Gaps 
\begin{itemize}
		\item Methodology
		\item Experiment
		\item Results
\end{itemize}
\item Evaluation of Music Recommender Systems
	\begin{itemize}
		\item Methodology
		\item Results
	\end{itemize}
\item Discussion (Analysis of Findings)
\item Future research
\item Conclusions
\end{itemize}

\section{Publications}
I have presented a poster on the research conducted so far, at the 3rd Annual EECS Postgraduate Conference and C4DM 10th Anniversary event.\\
I have co-authored a paper titled ``Music Discovery with Social Networks" (Appendix C), which I shall be presenting on October 23, 2011 at Workshop on Music Recommendation and Discovery\footnote{\url{http://womrad.org/2011/}} in conjunction with ACM RecSys\footnote{\url{http://recsys.acm.org/2011/index.shtml}}.\\
%In April 2012, experiment 2 (partially completed application) mashup shall be submitted for the AI Mash-Up Challenge 2012\footnote{\url{https://sites.google.com/a/fh-hannover.de/aimashup11/}}.\\
Upon completion of the Study One:``Comparison of Standard (ILM and Last.fm) and Musicological (ILM) annotated mood metadata'' before 18th January 2012, a paper will be submitted to the 9th International Symposium on Computer Music Modeling and Retrieval (CMMR): \emph{Music and Emotions} for presentation\\
% or published in Computer Music Journal\footnote{\url{http://www.computermusicjournal.org/}}.\\
In June 2012, the results from Study Two:``Auto-tagging commercial music with mood tags" will be submitted as a conference paper to ACM RecSys or ISMIR\\
Upon completion of Study Three:``Development of Mood Ontology to Bridge Semantic Gaps in MusicMood Classfication" in June 2013, the results from the project will be submitted to ACM RecSys or ISMIR 2013.\\
In June 2013, I will start writing my thesis and prepare a journal based on evaluation of studies conducted which shall be submitted to Journal of New Music Research or Computer Music Journal in September 2013.\\
\section{Submissions to Relevant Conferences and Journals}
Upon completion of each of the studies, submission to following conferences and journals will be considered:  
\begin{itemize}
\item International Symposium on Computer Music Modeling and Retrieval (CMMR)
\item The Extended Semantic Web Conference\footnote{\url{http://2012.eswc-conferences.org/}} (ESWC)
\item Computer Music Journal\footnote{\url{http://www.computermusicjournal.org/}} (CMJ)
\item Journal of New Music Research\footnote{\url{http://www.tandf.co.uk/journals/nnmr}}
\item ISMIR
\item ACM RecSys
%http://www.dai-labor.de/carr2011/
% UIST (ACM Symposium on User Interface Software and Technology). 
\end{itemize}

\section{Future Personal and Skills Development}
Over the next two years, personal and skills development will include the following Learning Institute courses:
\begin{itemize}
\item Certificate in Learning and Teaching (CILT)
\item Research Quality and Impact
\item Supervision of MSc Students
\item Critical Thinking
\item Introduction to iTunesU 
\item Self Motivation During Your PhD
\item Writing your Thesis
\item The Viva
\end{itemize} 

\begin{table}
\caption{Research Plan}
\centering
\begin{tabular}{p{5.7cm} l}
\hline\hline
Task & Date of Completion\\ [0.5ex]
\hline
Collection, comparison and categorisation of mood-related tagging terms used in Standard (ILM and Last.fm) and Musicological (ILM) annotated metadata & January 2012\\
\hline
Auto-tagging commercial music with mood tags & June 2012\\
\hline
Stage 2 & September 2012\\
\hline
Development of Mood Ontology & December 2012\\
\hline
Extension of Music Ontology with Mood Ontology & June 2013\\
\hline
Evaluation of Proposed Music Recommender Systems and Future Work & September 2013\\
\hline
Writting-up status & July 2013\\
\hline
Thesis Submission & December 2013\\
\hline
\end{tabular}
\label{table:rp}
\end{table} 
